Identification of Generalized Dynamic Factor Models from mixed-frequency data

Modeling of high dimensional time series by linear time series models such as vector autoregressive models is often marred by the so-called "curse of dimensionality". In order to overcome this problem generalized linear dynamic factor models (GDFM's) maybe used. In high-dimensional time series the single univariate time series are often sampled at different frequencies. This is the so-called mixed-frequency situation. We consider identifiability of the underlying high-frequency GDFM (i.e. the GDFM generating the data at the highest sampling frequency occurring) in the case of mixed frequency data and we shortly describe two estimation procedures in this situation based on the EM algorithm. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.